A Call for Low-Hanging Fish

How do you get people to believe in the power of analytics? I’ve been writing, speaking, and hosting conferences about it for a decade, and I’m on the prowl for new ideas.

The time-honored tradition is to show colleagues examples of how they can make more, spend less, and improve customer satisfaction – by the numbers. First, you start with case studies from others just to get their attention. Then, it’s time to run a few reports and some minor tests here and there to show them how their own Web property would benefit from experimentation and measurement.

Usually, you can find a couple of kindred spirits in the organization who are curious enough to give it a shot. These are open-minded people whose sense of self is not inextricably intermeshed with their ability to look like the most intuitive person in the room – they are willing to try things and change their minds based on outcomes.

From these small experiments come the first fruits of their labors – the low-hanging fruits. Dozens of Web site problems can be immediately recognized by the likes of Bryan Eisenberg, Tim Ash, or even a handful of your customers. The first round of simple tests and landing page optimizations will yield excellent, illustrative results. The low-hanging fruit is a wonderful, sweet way to convince people that they should grab some baskets and start collecting more.

Unfortunately, only a limited amount of fruit can be harvested from the ground or plucked from easy-to-reach, overhead branches. If an organization is to truly benefit from analytics, its members must be willing to fashion ladders to reach the higher elevations and engage in data irrigation and cultivation to maintain a steady crop of results.

That’s where the fish come in. We need a variety of easy, beginner, analytical processes that any novice can attempt. Instead of giving them fish, we must teach them to fish. And like any other educational endeavor, these processes must come with some assurance of some modest level of success. The trout farm lets them get the hang of it before giving up out of boredom and dejection.

So, I’m putting out a call for low-hanging fish.

What types of introductory, test-and-measure, optimization activities would you recommend to a neophyte? Nothing overly sophisticated, nothing n-dimensional, and nothing that will require an advanced degree in statistical theory, a hand-coded, dynamic content serving system, or more than a couple of hours to implement.